The goal of hydrocarbon exploration is to find porous and permeable geologic deposits containing high pore-space saturations of hydrocarbons, under sufficient pressure to allow so me mode of commercial production. In pursuit of this goal, companies, countries and individuals collect and process many types of geophysical and geological data. The data is often analyzed to find anomalous zones that can reasonably be attributed to the presence of hydrocarbons.
The usage of 2D and 3D seismic data anomalies has been a standard practice in the petroleum industry since the 1960s. Other geologic and geophysical data anomalies have been tried, sometimes successfully, for over a century. These include various gravimetric, electromagnetic, chem ical, biological and speculative methods.
The usage of anomalies for oil and gas detection has been plagued by several problems. First, most remote sensing anomalies (e.g., a 3D seismic amplitude anomaly) cannot be directly tied to a rock property that could be measured in the laboratory or using well logs. Much effort is expended attempting to tie observed anomalies to known rock responses by modeling the expected attribute response or otherwise correlating with a known producing reservoir. This work is often based on the experience of the practitioner.
A second problem is that the anomalies themselves are often evaluated or tied to response models in a qualitative manner. With qualitative assessment as the basis, quantitative, objective and reproducible error analysis has not been possible.
A third problem is that a basic physical property at work in hydrocarbon reservoirs is that both oil and gas are less dense than water. This generally causes oil and gas to accumulate up-structure in the pore-space of potential reservoir rocks. The higher water saturations are found, generally, down-structure. This separation of saturations is driven by gravity. When such a separation of fluid types occurs, flat interfaces, in depth, are expected to form.
This separation causes numerous possible classes of data attribute response. First, the hydrocarbon reservoir will have one response for each hydrocarbon type. The water-saturated part of the reservoir may have a second data response and the interfacial area a third type of attribute data response. This sequence of responses in the processed attribute data allows for a simultaneous analysis of the three classes.
Another problem is that the strength of many types of data attribute anomalies is dependent on the rock physics of the geologic systems. Some anomalies are very evident in the data. Others can be very subtle and cause considerable debate. Another associated problem is that much work in hydrocarbon exploration continues to be done in areas where the data are poor, noisy or difficult to interpret. In areas of good data quality, many high-strength anomalies are adequately interpreted by inspection. As the data quality and/or imagining ability of the data degrade, it can be very difficult to verify that a legitimate anomaly does or does not exist in a given set of data, especially when the rock physics suggests that any meaningful anomaly would be subtle.
The lack of quantification, error analysis, subjectivity of analysis and data quality issues cause variations in the appraisal of data anomalies in oil and gas exploration and production projects. It is not uncommon for different individuals or companies to examine the same anomaly and reach irreconcilably, different conclusions. In many cases, it has not been possible to explain quantitatively why the anomaly of one prospect should be “believed or trusted” more than that of another prospect. This causes different entities to make drastically different investment decisions concerning prospects based on the same underlying data.
The present invention is designed for the quantification and evaluation of data anomalies in the search for producible hydrocarbon deposits. It is designed to simultaneously quantify and summarize the hydrocarbon reservoir part of the anomaly, the water reservoir part of the data and the interfacial zone. The invention addresses the case of multiple hydrocarbon zones, e.g., gas over oil over water. It is designed to specifically test the model wherein gas is less dense than oil and oil is less dense than water, with data responses varying by structural position.
This current invention can also be used for the quantification of changes in lithology, facies, or rock fabric from one location to another. It is designed to function in areas of low signal-to-noise and aid in the determination of data suitability for hydrocarbon detection for the expected rock physics environment. This allows the invention to be applied to the detection of subtle hydrocarbon related data anomalies.
This invention is also designed to quantify responses and quantify response uncertainties in a manner that can be consistently defined, reported and replicated by others. Quantification and replication make the output of this invention suitable for quantitative comparison with rock physics analyses, petro-physical analyses, response modeling and geologic analyses (e.g., fit to structure analysis). This combination of capabilities represents an advance over current methods.